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About

Mehrdad Jazayeri joined the MIT faculty in January 2013 as an assistant professor in the Department of Brain and Cognitive Sciences and an Investigator in the McGovern Institute. Jazayeri, who is originally from Iran, obtained a B.Sc in Electrical Engineering from Sharif University of Technology in Tehran. He received his PhD from New York University, where he studied with J. Anthony Movshon, winning the 2007 Dean’s award for the most outstanding dissertation in the university. After graduating, he was awarded a Helen Hay Whitney fellowship to join the laboratory of Michael Shadlen at the University of Washington, where he was a postdoctoral researcher.

Research

When we think of behavior, we think of neural code, and when we think of neurons and neuronal networks, we think of neural dynamics. The link between brain and behavior thus resides at the intersection between the neural code and neural dynamics. The long-term objective of research in my lab is to understand (1) how neurons and neural circuits generate and control dynamic patterns of activity, and (2) how those patterns encode behaviorally relevant information.

We tackle these question by performing experiments on animals trained to perform a wide array of cognitive tasks that require anticipation, integration, coordination, and timing – functions that depend on the brain’s internally-generated dynamics. We combine psychophysics, electrophysiology, optogenetics, machine learning, and computational modeling to uncover the mechanisms that shape neural dynamics, and the principles that put those dynamics to use in the control of behavior.

Teaching

9.S912: Quantitative Methods and Computational Models in Neuroscience

This course provides students with the theoretical background and practical skills needed to analyze and model neurobiological observations at the molecular, systems and cognitive levels. Students will develop an intuitive understanding of mathematical tools and computational techniques, and learn to apply this knowledge to analyze, visualize and model research data using MATLAB programming. Topics include: Linear Systems and Operations, Dimensionality Reduction (e.g. PCA), Bayesian Approaches, Descriptive and Generative Models, Classification and Clustering, and Dynamical Systems.